Xinyu Mao

Step Reduced K-Best Sphere Decoding

We propose an algorithm that reduces the complexity of the K-best sphere decoding (K-best SD) algorithm, which is a powerful parallel detection algorithm for multiple-input multiple-output systems (MIMO). By analyzing the probability of different nodes to be the final solution, the algorithm prunes some nodes during the tree search to reduce the complexity. Simulation results prove that compared with the K-best SD algorithm the proposed algorithm performance drops very little. Compared with the famous fixed-complexity sphere decoding (FSD) with the same complexity, the proposed algorithm has better performance.

A Reduced Complexity K-Best SD Algorithm Based on Chi-Square Distribution for MIMO Detection

A reduced K-best sphere decoding (K-best SD) algorithm for Multiple-Input Multiple-Output (MIMO) detection is proposed. The algorithm reduces the complexity of the K-best SD by combining the statistics character of the signal and the requirement of the quality of service (QoS). In the reducing processing of the proposed algorithm, the chi-square distribution (CSD) property of the signal, the optimal symbol error rate (SER) property and the loss of pruning are considered together to give a theoretic error bound and then a threshold to determined which route can be pruned to reduced the calculation complexity. The algorithm reduces the complexity with a controllable cost of performance decrease. Simulation results on a 16QAM system with 4×4 antennas show that the algorithm can attain the near-optimal performance with a significant complexity reduction comparing to the original K-best SD or maximum likelihood (ML) algorithm.

A new dynamic K-best SD algorithm for MIMO detection

Multiple Input Multiple Output (MIMO) system is considered as an unalterable technology in wireless communication for its advantages in the spectral efficiency. Among the detection algorithms, maximum likelihood (ML) detection can achieve the best bit error rate performance, but the computational complexity of ML detection is too huge to be acceptable. In order to solve this problem, numbers of algorithms have been proposed. The K-Best SD sphere decoding (K-Best SD) algorithm is one of them. As K increase, the K-Best SD algorithm will approach the bit error rate of ML detection. However, if the K is large, the computational complexity will be unacceptable. In this paper, we propose a modified K-Best SD algorithm, in which the difference between the partial Euclidean distance of best and second best solution at each level of the tree search can be used to calculate the dynamic K, which can reduce the computational complexity considerably with a negligible BER performance loss.

Reduced K-best sphere decoding algorithm based on minimum route distance and noise variance

This paper focuses on reducing the complexity of Kbest sphere decoding(SD) algorithm for the detection of uncoded multiple input multiple output(MIMO) systems. The proposed algorithm utilizes the thresholdpruning method to cut nodes with partial Euclidean distances(PEDs) larger than the threshold. Both the known noise value and the unknown noise value are considered to generate the threshold, which is the sum of the two values. The known noise value is the smallest PED of signals in the detected layers. The unknown noise value is generated by the noise power, the quality of service(QoS) and the signaltonoise ratio(SNR) bound. Simulation results show that by considering both two noise values, the proposed algorithm makes an efficient reduction while the performance drops little.

Two Block Partitioned Dijkstra Algorithms

The Dijkstra algorithm (DA) is a kind of tree search algorithm. The biggest advantage is that it has the smallest number of visited nodes among all optimal tree search algorithms. But stack sizes required by the DA are always too large to achieve. By partitioning the searching tree into blocks, two modified algorithms are proposed in this article to shrink stack sizes. One, serial block partitioned DA, searches blocks one by one. Another, parallel block partitioned DA, searches blocks at the same time. Radii, which are updated when one block search is finished, are set to cut nodes with metrics larger than them in both algorithms. Simulation results show that the visited nodes number of serial block partitioned DA increase is very limited while the stack size is reduced exponentially. It also shows that stack sizes of the parallel block partitioned DA are reduced exponentially and the processing time is reduced efficiently. The performance of proposed algorithms is kept optimal in both proposed algorithms.

Conditional Single Tree Search Sphere Decoding

Information iteratively updating between the detection and the decoding in multiple-input multiple-output (MIMO) systems is a promising method to access the channel capacity. However, the complexity of detection algorithms is very high even if no iteration is considered. Single tree search is a very powerful detection algorithm in the iterative detection of MIMO, which combines tree searches of all byte into a single tree search (STS). But inherited from the complexity of nonlinear detection algorithm, the STS algorithm is still too complicated in many scenarios. In this article, we proposed that byte whose values of log likelihood ratio (LLR) are large enough are good enough for detecting and need no further updating. We set threshold in detection. A tree where LLR values of all byte are larger than it is free of update. As a result, a lot of calculation of search can be reduced, and the complexity can be reduced lot while the performance loss is very small. When BER=10-3, threshold=0.1 or 1, the performance loss is about 0.03dB or 0.16dB. When SNR=22dB, the complexity of the proposed algorithm is about 26.6% or 20.02% of the original algorithm.

Partial Noise Value Aided Reduced K-Best Sphere Decoding

This article focuses on reducing the complexity of K-best sphere decoding (K-best SD) algorithm for the detection of multiple-input multiple-output (MIMO) systems. One common reduction method is that one or more selected thresholds are set to cut excess nodes with partial Euclidean Distance (PED) larger than them. For a long time, statistical characteristic of noise has been well explored to generate thresholds. But the known noise in a certain specific transmission process is always overlooked. In this article, not only the statistical characteristic of noise is calculated, but also the known value of noise is considered. By adding a parameter determined by both noise and quality of service (QoS) to the smallest PED in each searching layer, a tighter and more suitable threshold can be calculated for this layer. Simulation results show that the proposed algorithm makes an efficient complexity reduction while the performance drops little. Specially, the proposed algorithm reduces the computational complexity about 90\% while the bit error ratio (BER) performance drops around 10\% in 4-by-4 MIMO systems employing 16-QAM or 64-QAM modulation. A new parameter, half complexity point, is proposed to evaluate the reduction effect, and half complexity points of the proposed algorithm are better than one selected original algorithm.

A New Noise Variance Based Layered Pruning ML-DFE Algorithm

A new noise variance based reduced maximum likelihood decision feedback equalization (ML-DFE) algorithm has been developed. This algorithm reduces the calculation complexity by exploring the intrinsic statistical properties layer by layer. Through setting layered thresholds, part of the nodes in the searching process will be cut by comparing with the thresholds. Simulation results show that the complexity drops lots while the performance drops small.

Reduced ML-DFE Algorithm

Multiple-input multiple-out (MIMO) technology is a very promising technology in the future high spectrum efficient wireless communication system. While the calculation complexities of most receive detection algorithms of the MIMO system are very high. Maximum likelihood decision feedback equalization (ML-DFE) algorithm is good at the balance of the performance and the complexity of detection. But it complexity is still high.

Layer reduced K-best sphere decoding

The detection of multiple-input multiple-output (MIMO) system is an important issue. The K-best sphere decoding (K-best SD) is a promising technology for the MIMO detection. But the complexity of the K-best SD is very high. In this article, a new algorithm is proposed to simply the complexity of the K-best SD. Through analyzing the statistics character of the channel matrix, we propose that the number of K can be reduced with the increasing of the detection layers. Simulation results show that with the price of slight performance drop the proposed algorithm can reduce the complexity of the K-best SD efficiently.